

'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=2		,					UpperCAmelCase__     :		str=3		,					UpperCAmelCase__     :		Optional[int]=64		,					UpperCAmelCase__     :		Union[str, Any]=None   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=np.random.default_rng(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=length
										lowercase :							List[Any]    		=rng.normal(size=(length,)   ).astype(np.floataa   )
										lowercase :							Dict    		=a * self.x + b + rng.normal(scale=0.1		,					size=(length,)   ).astype(np.floataa   )







				def __len__(       self     :		Tuple   ):
										'''simple docstring'''
										return self.length







				def __getitem__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										return {"x": self.x[i], "y": self.y[i]}





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		str=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Any    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[str]    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[Any]    		=True







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Any=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							Any    		=False
										return x * self.a[0] + self.b[0]





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		int=0		,					UpperCAmelCase__     :		Optional[Any]=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Optional[int]    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							int    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							Tuple    		=True







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							List[Any]    		=False
										return x * self.a + self.b






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     int = 16						)				->  Union[str, Any]:
						from datasets import load_dataset
						from transformers import AutoTokenizer

						lowercase :							Dict    		=AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						lowercase :							Optional[int]    		={'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
						lowercase :							Dict    		=load_dataset('''csv'''   , data_files=__magic_name__						)
						lowercase :							int    		=datasets['''train'''].unique('''label'''						)

						lowercase :							List[str]    		={v: i for i, v in enumerate(__magic_name__						)}

						def tokenize_function(__magic_name__						:     Dict						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase :							Dict    		=tokenizer(
												    examples['''sentence1''']   , examples['''sentence2''']   , truncation=__magic_name__   , max_length=__magic_name__   , padding='''max_length'''						)
												if "label" in examples:
																		lowercase :							List[Any]    		=[label_to_id[l] for l in examples['''label''']]
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						lowercase :							Optional[int]    		=datasets.map(
						    __magic_name__   , batched=__magic_name__   , remove_columns=['''sentence1''', '''sentence2''', '''label''']   , )

						def collate_fn(__magic_name__						:     Any						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(__magic_name__   , padding='''max_length'''   , max_length=128   , return_tensors='''pt'''						)
												return tokenizer.pad(__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)

						# Instantiate dataloaders.
						lowercase :							Union[str, Any]    		=DataLoader(tokenized_datasets['''train''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=2						)
						lowercase :							Tuple    		=DataLoader(tokenized_datasets['''validation''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=1						)

						return train_dataloader, eval_dataloader






def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')